


'''simple docstring'''


import argparse
import hashlib
import os
import urllib
import warnings

import torch
from torch import nn
from tqdm import tqdm

from transformers import WhisperConfig, WhisperForConditionalGeneration


UpperCAmelCase_						:  Dict				  =						{
    'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt',
    'tiny': 'https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt',
    'base.en': 'https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt',
    'base': 'https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt',
    'small.en': 'https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt',
    'small': 'https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt',
    'medium.en': 'https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt',
    'medium': 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt',
    'large': 'https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt',
    'large-v2': 'https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt',
}







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			int							= ["""layers""", """blocks"""]
				for k in ignore_keys:
								state_dict.pop(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)


UpperCAmelCase_						:  Tuple				  =						{
    'blocks': 'layers',
    'mlp.0': 'fc1',
    'mlp.2': 'fc2',
    'mlp_ln': 'final_layer_norm',
    '.attn.query': '.self_attn.q_proj',
    '.attn.key': '.self_attn.k_proj',
    '.attn.value': '.self_attn.v_proj',
    '.attn_ln': '.self_attn_layer_norm',
    '.attn.out': '.self_attn.out_proj',
    '.cross_attn.query': '.encoder_attn.q_proj',
    '.cross_attn.key': '.encoder_attn.k_proj',
    '.cross_attn.value': '.encoder_attn.v_proj',
    '.cross_attn_ln': '.encoder_attn_layer_norm',
    '.cross_attn.out': '.encoder_attn.out_proj',
    'decoder.ln.': 'decoder.layer_norm.',
    'encoder.ln.': 'encoder.layer_norm.',
    'token_embedding': 'embed_tokens',
    'encoder.positional_embedding': 'encoder.embed_positions.weight',
    'decoder.positional_embedding': 'decoder.embed_positions.weight',
    'ln_post': 'layer_norm',
}







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			List[str]							= list(s_dict.keys()	)
				for key in keys:
								_SCREAMING_SNAKE_CASE					:			Tuple							= key
								for k, v in WHISPER_MAPPING.items():
												if k in key:
																_SCREAMING_SNAKE_CASE					:			Tuple							= new_key.replace(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

								print(f"""{key} -> {new_key}"""	)

								_SCREAMING_SNAKE_CASE					:			str							= s_dict.pop(SCREAMING_SNAKE_CASE__	)
				return s_dict







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			int							= emb.weight.shape
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= nn.Linear(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, bias=SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Tuple							= emb.weight.data
				return lin_layer







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				os.makedirs(SCREAMING_SNAKE_CASE__				, exist_ok=SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= os.path.basename(SCREAMING_SNAKE_CASE__	)

				_SCREAMING_SNAKE_CASE					:			Tuple							= url.split("""/"""	)[-2]
				_SCREAMING_SNAKE_CASE					:			Any							= os.path.join(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	)

				if os.path.exists(SCREAMING_SNAKE_CASE__	) and not os.path.isfile(SCREAMING_SNAKE_CASE__	):
								raise RuntimeError(f"""{download_target} exists and is not a regular file"""	)

				if os.path.isfile(SCREAMING_SNAKE_CASE__	):
								_SCREAMING_SNAKE_CASE					:			Any							= open(SCREAMING_SNAKE_CASE__				, """rb"""	).read()
								if hashlib.shaaaa(SCREAMING_SNAKE_CASE__	).hexdigest() == expected_shaaaa:
												return model_bytes
								else:
												warnings.warn(f"""{download_target} exists, but the SHA256 checksum does not match; re-downloading the file"""	)

				with urllib.request.urlopen(SCREAMING_SNAKE_CASE__	) as source, open(SCREAMING_SNAKE_CASE__				, """wb"""	) as output:
								with tqdm(
								    total=int(source.info().get("""Content-Length"""	)	)				, ncols=80				, unit="""iB"""				, unit_scale=SCREAMING_SNAKE_CASE__				, unit_divisor=1024	) as loop:
												while True:
																_SCREAMING_SNAKE_CASE					:			Optional[int]							= source.read(8192	)
																if not buffer:
																				break

																output.write(SCREAMING_SNAKE_CASE__	)
																loop.update(len(SCREAMING_SNAKE_CASE__	)	)

				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= open(SCREAMING_SNAKE_CASE__				, """rb"""	).read()
				if hashlib.shaaaa(SCREAMING_SNAKE_CASE__	).hexdigest() != expected_shaaaa:
								raise RuntimeError(
								    """Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model."""	)

				return model_bytes







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				if ".pt" not in checkpoint_path:
								_SCREAMING_SNAKE_CASE					:			Any							= _download(_MODELS[checkpoint_path]	)
				else:
								_SCREAMING_SNAKE_CASE					:			Dict							= torch.load(SCREAMING_SNAKE_CASE__				, map_location="""cpu"""	)
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= original_checkpoint["""dims"""]
				_SCREAMING_SNAKE_CASE					:			Optional[int]							= original_checkpoint["""model_state_dict"""]
				_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= state_dict["""decoder.token_embedding.weight"""]
				remove_ignore_keys_(SCREAMING_SNAKE_CASE__	)
				rename_keys(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			int							= True
				_SCREAMING_SNAKE_CASE					:			List[Any]							= state_dict["""decoder.layers.0.fc1.weight"""].shape[0]

				_SCREAMING_SNAKE_CASE					:			str							= WhisperConfig(
				    vocab_size=dimensions["""n_vocab"""]				, encoder_ffn_dim=SCREAMING_SNAKE_CASE__				, decoder_ffn_dim=SCREAMING_SNAKE_CASE__				, num_mel_bins=dimensions["""n_mels"""]				, d_model=dimensions["""n_audio_state"""]				, max_target_positions=dimensions["""n_text_ctx"""]				, encoder_layers=dimensions["""n_audio_layer"""]				, encoder_attention_heads=dimensions["""n_audio_head"""]				, decoder_layers=dimensions["""n_text_layer"""]				, decoder_attention_heads=dimensions["""n_text_state"""]				, max_source_positions=dimensions["""n_audio_ctx"""]				, )

				_SCREAMING_SNAKE_CASE					:			List[Any]							= WhisperForConditionalGeneration(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			Dict							= model.model.load_state_dict(SCREAMING_SNAKE_CASE__				, strict=SCREAMING_SNAKE_CASE__	)
				if len(SCREAMING_SNAKE_CASE__	) > 0 and not set(SCREAMING_SNAKE_CASE__	) <= {
				    "encoder.embed_positions.weights",
				    "decoder.embed_positions.weights",
				}:
								raise ValueError(
								    """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
								    f""" but all the following weights are missing {missing}"""	)

				if tie_embeds:
								_SCREAMING_SNAKE_CASE					:			Optional[int]							= make_linear_from_emb(model.model.decoder.embed_tokens	)
				else:
								_SCREAMING_SNAKE_CASE					:			List[str]							= proj_out_weights

				model.save_pretrained(SCREAMING_SNAKE_CASE__	)


if __name__ == "__main__":
				UpperCAmelCase_						:  Optional[Any]				  =						argparse.ArgumentParser()
				# # Required parameters
				parser.add_argument('--checkpoint_path', type=str, help='Patht to the downloaded checkpoints')
				parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
				UpperCAmelCase_						:  Tuple				  =						parser.parse_args()

				convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)








'''simple docstring'''


import math







def      snake_case_  (    SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				assert isinstance(SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	) and (
				    number >= 0
				), "'number' must been an int and positive"

				if 1 < number < 4:
								# 2 and 3 are primes
								return True
				elif number < 2 or not number % 2:
								# Negatives, 0, 1 and all even numbers are not primes
								return False

				_SCREAMING_SNAKE_CASE					:			List[str]							= range(3				, int(math.sqrt(SCREAMING_SNAKE_CASE__	) + 1	)				, 2	)
				return not any(not number % i for i in odd_numbers	)







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__=1				, **SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Dict							= factor * value
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= value

				while not is_prime(SCREAMING_SNAKE_CASE__	):
								value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

				if value == first_value_val:
								return next_prime(value + 1				, **SCREAMING_SNAKE_CASE__	)
				return value




